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My opinion is that we should preserve NaN where they appear, but we also need to remember that standard equality says that NaN == NaN is false so we need specialise equality routines if we expect NaNs...
The text was updated successfully, but these errors were encountered:
We had a discussion earlier about how to deal with
NaN
values. In the JSON encoding,NaN
is getting turned intoNone
. We then have code that converts these back toNaN
(https://github.com/Cambridge-ICCS/ONEFlux/blob/27-generate-unit-tests-for-cpdassignustar/tests/conftest.py#L73) however we still have some places, e.g., here: https://github.com/Cambridge-ICCS/ONEFlux/blob/27-generate-unit-tests-for-cpdassignustar/tests/unit_tests/test_ustar_cp/test_cpdBootstrapUStarTh4Season20100901.py#L302 whereNone
is used. Are there any others?My opinion is that we should preserve
NaN
where they appear, but we also need to remember that standard equality says thatNaN == NaN
is false so we need specialise equality routines if we expect NaNs...The text was updated successfully, but these errors were encountered: